The pasta-on-wall test is supposed to tell you when spaghetti is done. Throw a strand at the wall. If it sticks, it’s cooked. If it slides off, keep boiling.
This doesn’t work. Properly cooked pasta has enough surface starch to stick regardless of doneness. Undercooked pasta sticks. Overcooked pasta sticks. The test measures stickiness, not doneness. You’re checking the wrong thing.
Organizations implement AI the same way. Deploy a model to production. If it sticks (doesn’t immediately break), call it done. If it slides off (obviously fails), try another approach. They’re measuring deployment success, not model validity. They’re checking the wrong thing.
Organizations implement AI the same way. Deploy a model to production. If it sticks (doesn’t immediately break), call it done. If it slides off (obviously fails), try another approach. They’re measuring deployment success, not model validity. They’re checking the wrong thing.
A 2021 Gartner survey found that 85% of AI projects fail to deliver expected business value. The 2019 MIT Sloan and BCG study showed that only 10% of companies get significant financial benefit from AI. These aren’t engineering problems. They’re measurement problems masquerading as innovation.
Why the Pasta Test Fails
The pasta-on-wall method checks for obvious failure. It doesn’t check for subtle failure, delayed failure, or failure that looks like success initially.
Undercooked pasta sticks to the wall for a few seconds, then slides off. You’re not watching the wall ten seconds later. You see it stick and conclude it’s done. You serve undercooked pasta.
AI deployment has the same problem. A model works in initial production testing. Metrics look fine. You declare success and move to the next project. Three months later, the model’s predictions have degraded because the data distribution shifted. Nobody noticed because nobody was watching. You served undercooked AI.
The test also fails because sticking is the wrong metric. Good pasta should be al dente, tender but with resistance. Sticking to walls doesn’t measure this. Tasting measures this. But tasting requires judgment and experience. Throwing is faster and requires no expertise.
Organizations prefer deployment testing over proper validation for the same reason. Proper validation requires expertise, time, and judgment about acceptable error rates. Deployment is faster and requires no difficult decisions. If the system runs without crashing, call it working.
What “Throwing Pasta” Actually Looks Like in AI
The pasta approach manifests as production testing without understanding. Deploy multiple models or approaches. See which one doesn’t break. Use that one. The model that sticks might be the worst performer that happens to not crash.
A financial services company deploys three different fraud detection models to production simultaneously. Model A catches 75% of fraud with 5% false positives. Model B catches 60% of fraud with 2% false positives. Model C catches 45% of fraud with 1% false positives.
Model C “sticks” because false positives generate customer complaints, which are visible and immediate. Missed fraud is invisible until months later when investigations complete. The company keeps Model C because it stuck. They’re letting 30% more fraud through to avoid visible complaints.
They’re measuring the wrong thing. Sticking means “doesn’t create immediate visible problems.” Working means “achieves the actual objective with acceptable tradeoffs.” These are different.
A content moderation system removes 90% of policy-violating content but also removes 15% of legitimate content incorrectly. The false removals generate appeals and negative sentiment. The company tunes the model to reduce false removals to 5%, which also reduces correct removals to 75%. More violating content stays up.
The tuned model “sticks better” because fewer users complain. More users experience harm from unmoderated content, but that harm is diffuse and doesn’t generate direct complaints to the company. The pasta test selects for minimizing visible complaints, not minimizing harm.
The Boiling Water Problem
The pasta test assumes you’re testing doneness. But you can’t test doneness if the pasta is still in boiling water. It continues cooking. You need to remove it from heat, let it stabilize, then test.
AI models continue “cooking” in production. The data distribution shifts. The model’s behavior changes. User behavior changes in response to the model. These feedback loops mean the model you deployed isn’t the model you’re running three months later.
A recommendation system suggests products users might like. Users buy the recommended products. The model observes these purchases and learns that users like these products. It recommends them more. The model is learning from its own outputs, not from organic user preferences. It’s creating a feedback loop that amplifies its initial biases.
You tested the model’s performance before deployment. That test measured behavior on historical data. It didn’t measure behavior in feedback loops because those loops don’t exist until production. The pasta is still in boiling water. You can’t test if it’s done.
Organizations handle this by deploying anyway and monitoring metrics. But metrics lag reality. By the time metrics show problems, thousands or millions of decisions have been made. The pasta is already on plates. Testing it now is too late.
Why Organizations Keep Throwing Pasta
The pasta method persists because it looks like agile development. Try things quickly. See what works. Iterate. This works for software where failures are obvious and reversible.
It fails for AI where failures are subtle and cumulative. A bug in traditional software either works or crashes. An AI model makes wrong predictions that look plausible. Nobody notices until much later, if ever.
The pasta method also appeals to organizations that don’t know how to evaluate AI properly. Evaluation requires statistical expertise, domain knowledge, and time. Deployment requires none of these. You throw solutions at production and see which ones stick.
This transfers risk to users and customers. They experience the failed attempts. The fraudulent transaction that got approved. The legitimate content that got removed. The biased hiring recommendation. These failures are externalities to the organization trying different AI approaches.
The consulting industry reinforces this pattern by selling experimentation as best practice. “Fail fast and learn.” “Iterate quickly.” These slogans work when failures are cheap and contained. They’re expensive and harmful when failures affect people’s money, speech, or opportunities.
What Testing Actually Requires
Proper testing means removing the pasta from boiling water and checking if it’s done. For AI, this means testing in conditions that approximate production without affecting users.
Shadow mode deployment runs the model in production without using its outputs. Compare predictions to human decisions or known outcomes. Measure error rates on production data without impacting users. Deploy only after error rates are acceptable on sufficient production volume.
This requires infrastructure most organizations don’t have. It delays deployment, which conflicts with pressure to move fast. It reveals problems that are easier to ignore. Organizations skip shadow mode and throw pasta at production instead.
Adversarial testing deliberately finds inputs that break the model. Generate edge cases. Test boundary conditions. Find adversarial examples. Measure failure rates on these inputs. Decide if those rates are acceptable.
This requires expertise most teams don’t have. It slows development. It uncovers uncomfortable truths about model fragility. Organizations skip adversarial testing and discover fragility in production instead.
Bias audits check for discriminatory patterns before deployment. This requires demographic data, legal knowledge, and statistical methods. It can reveal that your model discriminates, which means you can’t deploy it, which means the project fails.
Organizations skip bias audits. They deploy discriminatory models. They face lawsuits or regulatory action years later. The pasta landed on plates before anyone tasted it.
The Data Quality Failure Mode
The pasta test doesn’t work if you’re boiling something other than pasta. The stickiness test assumes wheat-based noodles with specific starch content. It fails for rice noodles, soba, or vegetables.
AI models trained on poor data fail regardless of testing methodology. The model learns patterns in the training data. If those patterns don’t match production reality, the model fails. No amount of wall-throwing fixes this.
Training data reflects past decisions, including bad ones. A hiring model trained on historical hires replicates existing demographic patterns, even if those patterns reflect discrimination. The model doesn’t know about discrimination. It knows the patterns in the data.
Label quality problems are endemic. Training data gets labeled by humans, often outsourced annotators paid per label. They rush. They misunderstand instructions. They disagree with each other. Your ground truth isn’t true. It’s noisy approximations from people optimizing for speed over accuracy.
Coverage gaps mean your training data includes common cases but not edge cases. Edge cases are expensive to collect. You launch with insufficient coverage. The model fails on edge cases in production. You collect more data and retrain. This cycle continues indefinitely because edge cases are infinite.
Organizations treat these as engineering problems solvable with better tools. They’re resource allocation problems. Good training data is expensive. Most organizations don’t allocate sufficient resources because they don’t understand the cost until after the model fails in production. They were testing stickiness when they should have been buying better ingredients.
When Throwing Pasta Works
Some applications tolerate the pasta method because failure is cheap and visible.
Content recommendations: A bad recommendation wastes a few seconds of user attention. Users ignore it. You iterate based on engagement metrics. The cost of getting it wrong is trivial. Throw pasta at this problem all day.
A/B testing environments: You run controlled experiments. You measure business metrics directly. Attribution is clean because you controlled for confounds. The wall gives you accurate feedback about doneness. The pasta test works when the wall is instrumented.
Low-stakes classification: Email spam filtering tolerates false positives and false negatives. The consequences are minor annoyance, not material harm. You can iterate aggressively without worrying about irreversible damage.
These applications succeed with experimental deployment because failure cost is low and feedback is fast. Organizations see these successes and generalize to applications where failure is expensive and feedback is slow. The pasta test works for spaghetti. They try it on soufflés.
The Deployment Gap
Models developed in notebooks fail in production for mechanical reasons unrelated to model quality.
These applications succeed with experimental deployment because failure cost is low and feedback is fast. Organizations see these successes and generalize to applications where failure is expensive and feedback is slow. The pasta test works for spaghetti. They try it on soufflés.
Why the Metaphor Persists
Organizations keep throwing pasta because it feels like progress. You deployed something. It didn’t immediately crash. You can report success to stakeholders. The fact that it’s slowly degrading or quietly discriminating won’t be visible for months.
The method also obscures responsibility. If you properly test before deployment and the model fails, you made a bad decision. If you throw pasta at production and it eventually fails, the environment changed or requirements shifted. Nobody’s fault.
The pasta approach appeals to organizations that don’t have AI expertise. You don’t need to understand the model to see if it sticks. You need to understand the model to know if it works correctly. Sticking is observable by anyone. Correctness requires judgment.
This creates a market for deployment-focused tools and consulting. “Deploy your AI model in minutes.” The deployment isn’t the hard part. The validation, monitoring, and maintenance are hard. But those don’t make good marketing copy. Companies sell pasta-throwing solutions and organizations buy them.
The cycle repeats because failed AI projects don’t generate institutional knowledge. The team that threw pasta and failed gets reassigned. New teams start fresh, armed with the same pasta-throwing methodology. The vendors and consultants who sold them pasta-throwing services have moved to the next client. Nobody connects the failures to the methodology.
The boring solution remains the same: stop throwing pasta. Build proper test infrastructure. Invest in data quality. Deploy to shadow mode first. Monitor production performance. Accept that AI is engineering, not experimentation. This takes years and produces no impressive demos. Organizations choose pasta-throwing and fail predictably.